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Record W2419083812 · doi:10.1038/srep14217

Effects of viruses on bacterial functions under contrasting nutritional conditions for four species of bacteria isolated from Hong Kong waters

2015· article· en· W2419083812 on OpenAlexaff
Hao Liu, Xiangcheng Yuan, Jie Xu, Paul J. Harrison, Lei He, Kedong Yin

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of British Columbia
FundersHong Kong University of Science and TechnologySun Yat-sen UniversityNational Natural Science Foundation of China
KeywordsEutrophicationBiologyBacteriaEcologyAbundance (ecology)Marine bacteriophageDominance (genetics)PopulationNutrient

Abstract

fetched live from OpenAlex

Free living viruses are ubiquitous in marine waters and concentrations are usually several times higher than the bacterial abundance. These viruses are capable of lysing host bacteria and therefore, play an important role in the microbial loop in oligotrophic waters. However, few studies have been conducted to compare the role of viruses in regulating bacterial abundance and heterotrophic activities between natural oligotrophic waters and anthropogenic influenced eutrophic waters. In this study, we examined viral effects on bacterial functions of four single bacterial species incubated with natural viral assemblages in seawater samples from eutrophic and oligotrophic waters. The viral-lysis of bacteria was significantly higher in eutrophic than oligotrophic waters. This suggests that viruses were capable of controlling bacterial abundance, respiration and production in the eutrophic waters. Cellular bacterial respiration and production was higher with viruses than without viruses, which was more evident in the oligotrophic waters. These results indicate that viruses can slow down bacterial consumption of oxygen and reduce bacteria-induced eutrophication effects in anthropogenic eutrophic waters, but switch to the role of sustaining the bacterial population when nutrients are limiting. There were bacterial species differences in resisting viral attack, which can influence the dominance and biodiversity of bacterial species in coastal waters.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.245
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2015
Admission routes1
Has abstractyes

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